Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations72
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.8 KiB
Average record size in memory154.0 B

Variable types

Numeric13
Boolean2
Unsupported1
Categorical4

Alerts

chance_of_snow has constant value "0" Constant
chance_of_rain is highly overall correlated with cloud and 1 other fieldsHigh correlation
cloud is highly overall correlated with chance_of_rain and 4 other fieldsHigh correlation
dewpoint_c is highly overall correlated with cloud and 2 other fieldsHigh correlation
feelslike_c is highly overall correlated with gust_kph and 7 other fieldsHigh correlation
gust_kph is highly overall correlated with feelslike_c and 5 other fieldsHigh correlation
heatindex_c is highly overall correlated with feelslike_c and 8 other fieldsHigh correlation
humidity is highly overall correlated with cloud and 9 other fieldsHigh correlation
is_day is highly overall correlated with feelslike_c and 3 other fieldsHigh correlation
precip_mm is highly overall correlated with cloud and 4 other fieldsHigh correlation
pressure_mb is highly overall correlated with humidity and 1 other fieldsHigh correlation
temp_c is highly overall correlated with feelslike_c and 7 other fieldsHigh correlation
uv is highly overall correlated with feelslike_c and 5 other fieldsHigh correlation
vis_km is highly overall correlated with precip_mmHigh correlation
will_it_rain is highly overall correlated with chance_of_rain and 3 other fieldsHigh correlation
wind_degree is highly overall correlated with wind_dir and 1 other fieldsHigh correlation
wind_dir is highly overall correlated with wind_degreeHigh correlation
wind_kph is highly overall correlated with feelslike_c and 7 other fieldsHigh correlation
windchill_c is highly overall correlated with feelslike_c and 7 other fieldsHigh correlation
vis_km is highly imbalanced (59.1%) Imbalance
condition is an unsupported type, check if it needs cleaning or further analysis Unsupported
precip_mm has 51 (70.8%) zeros Zeros
cloud has 1 (1.4%) zeros Zeros
uv has 36 (50.0%) zeros Zeros

Reproduction

Analysis started2025-05-14 13:32:37.986453
Analysis finished2025-05-14 13:32:47.779701
Duration9.79 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

temp_c
Real number (ℝ)

High correlation 

Distinct63
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.25
Minimum11.1
Maximum25.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-05-14T15:32:47.826698image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum11.1
5-th percentile11.6
Q113.575
median16.7
Q320.325
95-th percentile24.545
Maximum25.7
Range14.6
Interquartile range (IQR)6.75

Descriptive statistics

Standard deviation4.2856787
Coefficient of variation (CV)0.24844514
Kurtosis-0.98056345
Mean17.25
Median Absolute Deviation (MAD)3.45
Skewness0.43170212
Sum1242
Variance18.367042
MonotonicityNot monotonic
2025-05-14T15:32:47.904892image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.7 3
 
4.2%
12.9 3
 
4.2%
13 2
 
2.8%
14.7 2
 
2.8%
13.6 2
 
2.8%
24.3 2
 
2.8%
11.6 2
 
2.8%
22.2 1
 
1.4%
25.3 1
 
1.4%
25.7 1
 
1.4%
Other values (53) 53
73.6%
ValueCountFrequency (%)
11.1 1
1.4%
11.4 1
1.4%
11.5 1
1.4%
11.6 2
2.8%
11.8 1
1.4%
12 1
1.4%
12.1 1
1.4%
12.2 1
1.4%
12.4 1
1.4%
12.5 1
1.4%
ValueCountFrequency (%)
25.7 1
1.4%
25.3 1
1.4%
25.2 1
1.4%
24.6 1
1.4%
24.5 1
1.4%
24.4 1
1.4%
24.3 2
2.8%
23.9 1
1.4%
23.6 1
1.4%
23.3 1
1.4%

is_day
Boolean

High correlation 

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size648.0 B
True
42 
False
30 
ValueCountFrequency (%)
True 42
58.3%
False 30
41.7%
2025-05-14T15:32:47.978381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

condition
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size1.1 KiB

wind_kph
Real number (ℝ)

High correlation 

Distinct38
Distinct (%)52.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.716667
Minimum3.2
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-05-14T15:32:48.036535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum3.2
5-th percentile4.52
Q17.2
median11
Q315.575
95-th percentile20.68
Maximum22
Range18.8
Interquartile range (IQR)8.375

Descriptive statistics

Standard deviation5.2303017
Coefficient of variation (CV)0.44639844
Kurtosis-0.9330755
Mean11.716667
Median Absolute Deviation (MAD)3.8
Skewness0.34506144
Sum843.6
Variance27.356056
MonotonicityNot monotonic
2025-05-14T15:32:48.111661image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
7.2 6
 
8.3%
7.9 4
 
5.6%
13.3 4
 
5.6%
7.6 3
 
4.2%
13.7 3
 
4.2%
18.4 3
 
4.2%
10.8 3
 
4.2%
6.5 3
 
4.2%
10.4 2
 
2.8%
6.1 2
 
2.8%
Other values (28) 39
54.2%
ValueCountFrequency (%)
3.2 2
 
2.8%
3.6 1
 
1.4%
4.3 1
 
1.4%
4.7 1
 
1.4%
5 2
 
2.8%
6.1 2
 
2.8%
6.5 3
4.2%
6.8 2
 
2.8%
7.2 6
8.3%
7.6 3
4.2%
ValueCountFrequency (%)
22 2
2.8%
21.6 1
 
1.4%
20.9 1
 
1.4%
20.5 1
 
1.4%
20.2 2
2.8%
19.1 2
2.8%
18.7 1
 
1.4%
18.4 3
4.2%
18 1
 
1.4%
16.6 1
 
1.4%

wind_degree
Real number (ℝ)

High correlation 

Distinct52
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143.11111
Minimum90
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-05-14T15:32:48.188193image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile101.1
Q1121
median141
Q3165.25
95-th percentile187.15
Maximum270
Range180
Interquartile range (IQR)44.25

Descriptive statistics

Standard deviation30.960018
Coefficient of variation (CV)0.21633553
Kurtosis2.7587946
Mean143.11111
Median Absolute Deviation (MAD)23
Skewness1.0041196
Sum10304
Variance958.52269
MonotonicityNot monotonic
2025-05-14T15:32:48.266392image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
139 4
 
5.6%
166 4
 
5.6%
113 3
 
4.2%
143 3
 
4.2%
165 2
 
2.8%
167 2
 
2.8%
141 2
 
2.8%
118 2
 
2.8%
122 2
 
2.8%
147 2
 
2.8%
Other values (42) 46
63.9%
ValueCountFrequency (%)
90 1
1.4%
91 1
1.4%
94 1
1.4%
100 1
1.4%
102 1
1.4%
104 1
1.4%
105 1
1.4%
107 1
1.4%
108 1
1.4%
112 1
1.4%
ValueCountFrequency (%)
270 1
1.4%
209 1
1.4%
207 1
1.4%
191 1
1.4%
184 1
1.4%
178 1
1.4%
176 1
1.4%
174 1
1.4%
172 1
1.4%
170 1
1.4%

wind_dir
Categorical

High correlation 

Distinct7
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
SSE
20 
SE
19 
ESE
18 
S
E
Other values (2)

Length

Max length3
Median length3
Mean length2.4027778
Min length1

Characters and Unicode

Total characters173
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st rowESE
2nd rowESE
3rd rowESE
4th rowSE
5th rowSE

Common Values

ValueCountFrequency (%)
SSE 20
27.8%
SE 19
26.4%
ESE 18
25.0%
S 7
 
9.7%
E 4
 
5.6%
SSW 3
 
4.2%
W 1
 
1.4%

Length

2025-05-14T15:32:48.344139image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T15:32:48.411689image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
sse 20
27.8%
se 19
26.4%
ese 18
25.0%
s 7
 
9.7%
e 4
 
5.6%
ssw 3
 
4.2%
w 1
 
1.4%

Most occurring characters

ValueCountFrequency (%)
S 90
52.0%
E 79
45.7%
W 4
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 173
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 90
52.0%
E 79
45.7%
W 4
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 173
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 90
52.0%
E 79
45.7%
W 4
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 173
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 90
52.0%
E 79
45.7%
W 4
 
2.3%

pressure_mb
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1014.2222
Minimum1012
Maximum1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-05-14T15:32:48.474086image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1012
5-th percentile1013
Q11013
median1014
Q31015
95-th percentile1016
Maximum1017
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2011992
Coefficient of variation (CV)0.001184355
Kurtosis-0.96181077
Mean1014.2222
Median Absolute Deviation (MAD)1
Skewness0.30854405
Sum73024
Variance1.4428795
MonotonicityNot monotonic
2025-05-14T15:32:48.533578image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1013 23
31.9%
1014 19
26.4%
1015 14
19.4%
1016 13
18.1%
1012 2
 
2.8%
1017 1
 
1.4%
ValueCountFrequency (%)
1012 2
 
2.8%
1013 23
31.9%
1014 19
26.4%
1015 14
19.4%
1016 13
18.1%
1017 1
 
1.4%
ValueCountFrequency (%)
1017 1
 
1.4%
1016 13
18.1%
1015 14
19.4%
1014 19
26.4%
1013 23
31.9%
1012 2
 
2.8%

precip_mm
Real number (ℝ)

High correlation  Zeros 

Distinct22
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41069444
Minimum0
Maximum10.37
Zeros51
Zeros (%)70.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-05-14T15:32:48.592994image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.0675
95-th percentile2.398
Maximum10.37
Range10.37
Interquartile range (IQR)0.0675

Descriptive statistics

Standard deviation1.4558027
Coefficient of variation (CV)3.5447343
Kurtosis33.047536
Mean0.41069444
Median Absolute Deviation (MAD)0
Skewness5.4192318
Sum29.57
Variance2.1193615
MonotonicityNot monotonic
2025-05-14T15:32:48.657378image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 51
70.8%
0.12 1
 
1.4%
0.01 1
 
1.4%
0.43 1
 
1.4%
0.57 1
 
1.4%
0.34 1
 
1.4%
0.24 1
 
1.4%
3.22 1
 
1.4%
0.56 1
 
1.4%
0.54 1
 
1.4%
Other values (12) 12
 
16.7%
ValueCountFrequency (%)
0 51
70.8%
0.01 1
 
1.4%
0.04 1
 
1.4%
0.05 1
 
1.4%
0.12 1
 
1.4%
0.19 1
 
1.4%
0.24 1
 
1.4%
0.29 1
 
1.4%
0.34 1
 
1.4%
0.38 1
 
1.4%
ValueCountFrequency (%)
10.37 1
1.4%
5.49 1
1.4%
3.22 1
1.4%
2.64 1
1.4%
2.2 1
1.4%
0.66 1
1.4%
0.63 1
1.4%
0.6 1
1.4%
0.57 1
1.4%
0.56 1
1.4%

humidity
Real number (ℝ)

High correlation 

Distinct41
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.833333
Minimum31
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-05-14T15:32:48.721263image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile38.2
Q153.5
median71
Q390
95-th percentile95
Maximum96
Range65
Interquartile range (IQR)36.5

Descriptive statistics

Standard deviation20.148044
Coefficient of variation (CV)0.28444297
Kurtosis-1.2342552
Mean70.833333
Median Absolute Deviation (MAD)19
Skewness-0.36677543
Sum5100
Variance405.94366
MonotonicityNot monotonic
2025-05-14T15:32:48.789106image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
91 4
 
5.6%
93 4
 
5.6%
95 4
 
5.6%
90 4
 
5.6%
63 3
 
4.2%
88 3
 
4.2%
96 2
 
2.8%
60 2
 
2.8%
50 2
 
2.8%
71 2
 
2.8%
Other values (31) 42
58.3%
ValueCountFrequency (%)
31 1
1.4%
33 1
1.4%
34 1
1.4%
36 1
1.4%
40 2
2.8%
41 2
2.8%
42 1
1.4%
43 1
1.4%
45 1
1.4%
48 2
2.8%
ValueCountFrequency (%)
96 2
2.8%
95 4
5.6%
94 2
2.8%
93 4
5.6%
91 4
5.6%
90 4
5.6%
89 2
2.8%
88 3
4.2%
87 2
2.8%
86 1
 
1.4%

cloud
Real number (ℝ)

High correlation  Zeros 

Distinct41
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.069444
Minimum0
Maximum100
Zeros1
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-05-14T15:32:48.864832image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.55
Q113
median28
Q383.5
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)70.5

Descriptive statistics

Standard deviation36.294308
Coefficient of variation (CV)0.82357081
Kurtosis-1.3851597
Mean44.069444
Median Absolute Deviation (MAD)20.5
Skewness0.52362992
Sum3173
Variance1317.2768
MonotonicityNot monotonic
2025-05-14T15:32:48.939750image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
100 13
 
18.1%
7 4
 
5.6%
18 3
 
4.2%
28 3
 
4.2%
13 3
 
4.2%
8 3
 
4.2%
3 2
 
2.8%
25 2
 
2.8%
23 2
 
2.8%
21 2
 
2.8%
Other values (31) 35
48.6%
ValueCountFrequency (%)
0 1
 
1.4%
2 1
 
1.4%
3 2
2.8%
4 1
 
1.4%
7 4
5.6%
8 3
4.2%
9 2
2.8%
10 2
2.8%
12 1
 
1.4%
13 3
4.2%
ValueCountFrequency (%)
100 13
18.1%
93 1
 
1.4%
91 1
 
1.4%
90 1
 
1.4%
86 1
 
1.4%
85 1
 
1.4%
83 1
 
1.4%
78 1
 
1.4%
77 1
 
1.4%
76 1
 
1.4%

feelslike_c
Real number (ℝ)

High correlation 

Distinct60
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.193056
Minimum10.5
Maximum25.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-05-14T15:32:49.011822image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum10.5
5-th percentile11.165
Q112.8
median16.7
Q320.325
95-th percentile25.3
Maximum25.9
Range15.4
Interquartile range (IQR)7.525

Descriptive statistics

Standard deviation4.708408
Coefficient of variation (CV)0.27385522
Kurtosis-1.0120968
Mean17.193056
Median Absolute Deviation (MAD)3.85
Skewness0.43662659
Sum1237.9
Variance22.169106
MonotonicityNot monotonic
2025-05-14T15:32:49.092162image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.3 3
 
4.2%
16.7 3
 
4.2%
24.6 3
 
4.2%
12.7 2
 
2.8%
11.8 2
 
2.8%
25.3 2
 
2.8%
11.7 2
 
2.8%
24.7 2
 
2.8%
12.8 2
 
2.8%
19.1 1
 
1.4%
Other values (50) 50
69.4%
ValueCountFrequency (%)
10.5 1
1.4%
10.8 1
1.4%
10.9 1
1.4%
11 1
1.4%
11.3 1
1.4%
11.6 1
1.4%
11.7 2
2.8%
11.8 2
2.8%
12 1
1.4%
12.1 1
1.4%
ValueCountFrequency (%)
25.9 1
 
1.4%
25.7 1
 
1.4%
25.6 1
 
1.4%
25.3 2
2.8%
24.8 1
 
1.4%
24.7 2
2.8%
24.6 3
4.2%
24.2 1
 
1.4%
23.9 1
 
1.4%
21.9 1
 
1.4%

windchill_c
Real number (ℝ)

High correlation 

Distinct63
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.059722
Minimum10.5
Maximum25.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-05-14T15:32:49.195573image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum10.5
5-th percentile11.165
Q112.8
median16.7
Q320.325
95-th percentile24.545
Maximum25.7
Range15.2
Interquartile range (IQR)7.525

Descriptive statistics

Standard deviation4.5015071
Coefficient of variation (CV)0.26386755
Kurtosis-1.0620568
Mean17.059722
Median Absolute Deviation (MAD)3.85
Skewness0.36518036
Sum1228.3
Variance20.263566
MonotonicityNot monotonic
2025-05-14T15:32:49.276735image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.7 3
 
4.2%
14.3 3
 
4.2%
11.7 2
 
2.8%
12.8 2
 
2.8%
12.7 2
 
2.8%
24.3 2
 
2.8%
11.8 2
 
2.8%
25.3 1
 
1.4%
22.2 1
 
1.4%
25.7 1
 
1.4%
Other values (53) 53
73.6%
ValueCountFrequency (%)
10.5 1
1.4%
10.8 1
1.4%
10.9 1
1.4%
11 1
1.4%
11.3 1
1.4%
11.6 1
1.4%
11.7 2
2.8%
11.8 2
2.8%
12 1
1.4%
12.1 1
1.4%
ValueCountFrequency (%)
25.7 1
1.4%
25.3 1
1.4%
25.2 1
1.4%
24.6 1
1.4%
24.5 1
1.4%
24.4 1
1.4%
24.3 2
2.8%
23.9 1
1.4%
23.6 1
1.4%
23.3 1
1.4%

heatindex_c
Real number (ℝ)

High correlation 

Distinct59
Distinct (%)81.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.55
Minimum11.1
Maximum25.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-05-14T15:32:49.351318image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum11.1
5-th percentile11.6
Q113.575
median16.7
Q320.325
95-th percentile25.3
Maximum25.9
Range14.8
Interquartile range (IQR)6.75

Descriptive statistics

Standard deviation4.6933576
Coefficient of variation (CV)0.26742778
Kurtosis-1.0898449
Mean17.55
Median Absolute Deviation (MAD)3.45
Skewness0.48962194
Sum1263.6
Variance22.027606
MonotonicityNot monotonic
2025-05-14T15:32:49.431271image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.9 3
 
4.2%
24.6 3
 
4.2%
16.7 3
 
4.2%
24.5 2
 
2.8%
11.6 2
 
2.8%
13 2
 
2.8%
14.8 2
 
2.8%
25.3 2
 
2.8%
24.7 2
 
2.8%
13.6 2
 
2.8%
Other values (49) 49
68.1%
ValueCountFrequency (%)
11.1 1
1.4%
11.4 1
1.4%
11.5 1
1.4%
11.6 2
2.8%
11.8 1
1.4%
12 1
1.4%
12.1 1
1.4%
12.2 1
1.4%
12.4 1
1.4%
12.5 1
1.4%
ValueCountFrequency (%)
25.9 1
 
1.4%
25.7 1
 
1.4%
25.6 1
 
1.4%
25.3 2
2.8%
24.8 1
 
1.4%
24.7 2
2.8%
24.6 3
4.2%
24.5 2
2.8%
24.4 1
 
1.4%
24.2 1
 
1.4%

dewpoint_c
Real number (ℝ)

High correlation 

Distinct50
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.129167
Minimum5.9
Maximum15.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-05-14T15:32:49.511665image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5.9
5-th percentile7.21
Q110
median11.35
Q312.525
95-th percentile14.69
Maximum15.2
Range9.3
Interquartile range (IQR)2.525

Descriptive statistics

Standard deviation2.1969529
Coefficient of variation (CV)0.19740498
Kurtosis-0.17754324
Mean11.129167
Median Absolute Deviation (MAD)1.3
Skewness-0.31011078
Sum801.3
Variance4.8266021
MonotonicityNot monotonic
2025-05-14T15:32:49.587440image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.8 4
 
5.6%
10.5 4
 
5.6%
11.4 4
 
5.6%
13.1 3
 
4.2%
11.7 3
 
4.2%
10 2
 
2.8%
10.6 2
 
2.8%
11.3 2
 
2.8%
11.5 2
 
2.8%
11.2 2
 
2.8%
Other values (40) 44
61.1%
ValueCountFrequency (%)
5.9 1
1.4%
6 1
1.4%
6.9 1
1.4%
7.1 1
1.4%
7.3 1
1.4%
7.5 1
1.4%
7.6 1
1.4%
8 1
1.4%
8.1 1
1.4%
8.4 1
1.4%
ValueCountFrequency (%)
15.2 1
1.4%
15.1 1
1.4%
15 1
1.4%
14.8 1
1.4%
14.6 1
1.4%
14.5 1
1.4%
14.3 1
1.4%
14.1 1
1.4%
13.8 1
1.4%
13.6 1
1.4%

will_it_rain
Boolean

High correlation 

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size648.0 B
False
52 
True
20 
ValueCountFrequency (%)
False 52
72.2%
True 20
 
27.8%
2025-05-14T15:32:49.648546image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

chance_of_rain
Categorical

High correlation 

Distinct4
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
51 
100
19 
76
 
1
70
 
1

Length

Max length3
Median length1
Mean length1.5555556
Min length1

Characters and Unicode

Total characters112
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)2.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 51
70.8%
100 19
 
26.4%
76 1
 
1.4%
70 1
 
1.4%

Length

2025-05-14T15:32:49.713439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T15:32:49.774297image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 51
70.8%
100 19
 
26.4%
76 1
 
1.4%
70 1
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 90
80.4%
1 19
 
17.0%
7 2
 
1.8%
6 1
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 112
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 90
80.4%
1 19
 
17.0%
7 2
 
1.8%
6 1
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 112
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 90
80.4%
1 19
 
17.0%
7 2
 
1.8%
6 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 112
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 90
80.4%
1 19
 
17.0%
7 2
 
1.8%
6 1
 
0.9%

chance_of_snow
Categorical

Constant 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
72 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters72
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 72
100.0%

Length

2025-05-14T15:32:49.835803image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T15:32:49.886831image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 72
100.0%

Most occurring characters

ValueCountFrequency (%)
0 72
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 72
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 72
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 72
100.0%

vis_km
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
10.0
59 
2.0
7.0
 
3
5.0
 
1
9.0
 
1

Length

Max length4
Median length4
Mean length3.8194444
Min length3

Characters and Unicode

Total characters275
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)2.8%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row10.0

Common Values

ValueCountFrequency (%)
10.0 59
81.9%
2.0 8
 
11.1%
7.0 3
 
4.2%
5.0 1
 
1.4%
9.0 1
 
1.4%

Length

2025-05-14T15:32:49.943317image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-14T15:32:50.002766image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
10.0 59
81.9%
2.0 8
 
11.1%
7.0 3
 
4.2%
5.0 1
 
1.4%
9.0 1
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 131
47.6%
. 72
26.2%
1 59
21.5%
2 8
 
2.9%
7 3
 
1.1%
5 1
 
0.4%
9 1
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 275
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 131
47.6%
. 72
26.2%
1 59
21.5%
2 8
 
2.9%
7 3
 
1.1%
5 1
 
0.4%
9 1
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 275
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 131
47.6%
. 72
26.2%
1 59
21.5%
2 8
 
2.9%
7 3
 
1.1%
5 1
 
0.4%
9 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 275
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 131
47.6%
. 72
26.2%
1 59
21.5%
2 8
 
2.9%
7 3
 
1.1%
5 1
 
0.4%
9 1
 
0.4%

gust_kph
Real number (ℝ)

High correlation 

Distinct57
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.755556
Minimum4.6
Maximum27.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-05-14T15:32:50.066950image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4.6
5-th percentile5.855
Q111.825
median15.3
Q319.8
95-th percentile25.145
Maximum27.7
Range23.1
Interquartile range (IQR)7.975

Descriptive statistics

Standard deviation5.6131106
Coefficient of variation (CV)0.35626231
Kurtosis-0.64348539
Mean15.755556
Median Absolute Deviation (MAD)4.05
Skewness-0.0095945247
Sum1134.4
Variance31.507011
MonotonicityNot monotonic
2025-05-14T15:32:50.140999image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.1 4
 
5.6%
22 3
 
4.2%
13.9 2
 
2.8%
17.1 2
 
2.8%
15.3 2
 
2.8%
14.7 2
 
2.8%
10.3 2
 
2.8%
11.3 2
 
2.8%
12 2
 
2.8%
23.2 2
 
2.8%
Other values (47) 49
68.1%
ValueCountFrequency (%)
4.6 1
1.4%
5 1
1.4%
5.5 1
1.4%
5.8 1
1.4%
5.9 1
1.4%
6.5 1
1.4%
8 1
1.4%
8.4 1
1.4%
9.4 1
1.4%
9.6 1
1.4%
ValueCountFrequency (%)
27.7 1
 
1.4%
25.3 2
2.8%
25.2 1
 
1.4%
25.1 1
 
1.4%
24.8 1
 
1.4%
23.6 1
 
1.4%
23.2 2
2.8%
22 3
4.2%
21.1 4
5.6%
20.7 1
 
1.4%

uv
Real number (ℝ)

High correlation  Zeros 

Distinct25
Distinct (%)34.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95694444
Minimum0
Maximum5.4
Zeros36
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-05-14T15:32:50.206006image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.05
Q30.875
95-th percentile4.89
Maximum5.4
Range5.4
Interquartile range (IQR)0.875

Descriptive statistics

Standard deviation1.6481168
Coefficient of variation (CV)1.7222701
Kurtosis1.4711493
Mean0.95694444
Median Absolute Deviation (MAD)0.05
Skewness1.7057401
Sum68.9
Variance2.7162891
MonotonicityNot monotonic
2025-05-14T15:32:50.272733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 36
50.0%
0.3 4
 
5.6%
0.4 4
 
5.6%
0.1 3
 
4.2%
0.2 3
 
4.2%
4.8 2
 
2.8%
1.1 2
 
2.8%
1.8 1
 
1.4%
4.2 1
 
1.4%
0.5 1
 
1.4%
Other values (15) 15
20.8%
ValueCountFrequency (%)
0 36
50.0%
0.1 3
 
4.2%
0.2 3
 
4.2%
0.3 4
 
5.6%
0.4 4
 
5.6%
0.5 1
 
1.4%
0.6 1
 
1.4%
0.7 1
 
1.4%
0.8 1
 
1.4%
1.1 2
 
2.8%
ValueCountFrequency (%)
5.4 1
1.4%
5.3 1
1.4%
5.2 1
1.4%
5 1
1.4%
4.8 2
2.8%
4.5 1
1.4%
4.2 1
1.4%
3.7 1
1.4%
3.5 1
1.4%
3.1 1
1.4%

Interactions

2025-05-14T15:32:46.783342image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:38.372400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:39.040224image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:39.903387image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:40.595547image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:41.338795image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:41.979876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:42.715613image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:43.344565image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:44.021845image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:44.707428image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:45.518379image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:46.160464image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:46.831659image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:38.427931image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:39.094418image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:39.953767image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:40.648521image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:41.389321image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:42.030903image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:42.767908image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:43.399546image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:44.075457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:44.864728image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:45.569295image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:46.211478image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:46.884365image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:38.483734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:39.150258image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:40.010046image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:40.703015image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:41.442959image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:42.087055image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:42.821733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:43.456405image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:44.131322image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:44.920356image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:45.620076image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:46.263151image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:46.935770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:38.540988image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:39.203365image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:40.061313image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:40.754252image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:41.496197image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:42.138331image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:42.874903image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:43.510155image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:44.184038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:44.976689image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:45.670363image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:46.316289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:46.983092image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:38.590157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:39.255005image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:40.114987image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:40.801621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:41.543041image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:42.186149image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:42.922256image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:43.561339image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:44.235024image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:45.027089image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:45.716339image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:46.361120image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:47.028839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:38.639240image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:39.305743image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:40.169958image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:40.850657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:41.586833image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:42.235199image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:42.967367image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:43.611228image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:44.288783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:45.075869image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:45.766871image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:46.406368image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:47.076339image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:38.689807image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:39.360569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:40.223954image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:40.981398image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:41.635320image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:42.284719image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:43.014438image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:43.662206image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:44.340572image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:45.157181image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:45.818469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:46.451761image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:47.122063image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:38.737061image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:39.596690image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:40.282601image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:41.029041image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:41.681734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:42.331937image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:43.059231image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:43.711770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:44.390868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:45.206303image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:45.869314image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:46.496151image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:47.174210image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:38.791382image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:39.652883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:40.337985image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:41.085836image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:41.737711image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:42.385439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:43.111764image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:43.766126image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:44.446454image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:45.260918image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:45.926138image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:46.545926image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:47.224910image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:38.846190image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:39.707718image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:40.395234image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:41.141457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:41.790221image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:42.439450image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:43.163458image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:43.820372image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:44.502770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:45.317861image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:45.978951image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:46.597597image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:47.275552image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:38.900239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:39.762076image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:40.450892image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:41.194326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:41.841399image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:42.489287image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:43.213315image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:43.875927image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:44.557231image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:45.372230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:46.029860image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:46.648100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:47.319648image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:38.948477image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:39.808891image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:40.500504image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:41.242816image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:41.887633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:42.534174image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:43.256777image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:43.924827image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:44.605312image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:45.420623image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:46.071543image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:46.695517image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:47.363701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:38.993494image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:39.854979image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:40.546755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:41.291017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:41.932896image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:42.579553image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:43.298778image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:43.973620image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:44.654344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:45.468964image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:46.114444image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-14T15:32:46.738274image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-05-14T15:32:50.328875image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
chance_of_rainclouddewpoint_cfeelslike_cgust_kphheatindex_chumidityis_dayprecip_mmpressure_mbtemp_cuvvis_kmwill_it_rainwind_degreewind_dirwind_kphwindchill_c
chance_of_rain1.0000.5670.3180.1560.0870.2690.3460.0000.1170.2360.2530.1630.1830.9860.2080.2610.2190.169
cloud0.5671.0000.549-0.349-0.290-0.3280.5420.0000.766-0.435-0.345-0.3500.0000.8630.0780.171-0.208-0.361
dewpoint_c0.3180.5491.0000.035-0.0630.0600.4260.3600.616-0.3660.033-0.3060.2930.6070.1710.111-0.0520.019
feelslike_c0.156-0.3490.0351.0000.5810.995-0.8600.590-0.2610.3420.9940.5520.2350.2620.4100.0000.6190.999
gust_kph0.087-0.290-0.0630.5811.0000.607-0.5360.223-0.1620.3370.6110.4690.0000.3000.4750.3220.9490.579
heatindex_c0.269-0.3280.0600.9950.6071.000-0.8540.711-0.2260.3320.9980.5630.1890.5070.4410.1830.6480.993
humidity0.3460.5420.426-0.860-0.536-0.8541.0000.4070.521-0.503-0.861-0.6560.0000.457-0.2830.095-0.571-0.864
is_day0.0000.0000.3600.5900.2230.7110.4071.0000.1120.0000.7180.4120.1790.0000.2240.2770.3330.592
precip_mm0.1170.7660.616-0.261-0.162-0.2260.5210.1121.000-0.554-0.230-0.1760.7470.3740.2840.000-0.025-0.264
pressure_mb0.236-0.435-0.3660.3420.3370.332-0.5030.000-0.5541.0000.3360.0950.0880.498-0.0090.0000.2870.342
temp_c0.253-0.3450.0330.9940.6110.998-0.8610.718-0.2300.3361.0000.5710.1850.4830.4440.1690.6540.996
uv0.163-0.350-0.3060.5520.4690.563-0.6560.412-0.1760.0950.5711.0000.0000.0000.3920.0000.6250.555
vis_km0.1830.0000.2930.2350.0000.1890.0000.1790.7470.0880.1850.0001.0000.4340.0000.0000.0000.235
will_it_rain0.9860.8630.6070.2620.3000.5070.4570.0000.3740.4980.4830.0000.4341.0000.3780.2590.4390.282
wind_degree0.2080.0780.1710.4100.4750.441-0.2830.2240.284-0.0090.4440.3920.0000.3781.0000.8040.5270.413
wind_dir0.2610.1710.1110.0000.3220.1830.0950.2770.0000.0000.1690.0000.0000.2590.8041.0000.3880.075
wind_kph0.219-0.208-0.0520.6190.9490.648-0.5710.333-0.0250.2870.6540.6250.0000.4390.5270.3881.0000.618
windchill_c0.169-0.3610.0190.9990.5790.993-0.8640.592-0.2640.3420.9960.5550.2350.2820.4130.0750.6181.000

Missing values

2025-05-14T15:32:47.568255image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-14T15:32:47.718834image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

temp_cis_dayconditionwind_kphwind_degreewind_dirpressure_mbprecip_mmhumiditycloudfeelslike_cwindchill_cheatindex_cdewpoint_cwill_it_rainchance_of_rainchance_of_snowvis_kmgust_kphuv
datetime
2025-04-16 00:00:0012.4False{'text': 'Mist', 'icon': '//cdn.weatherapi.com/weather/64x64/night/143.png', 'code': 1030}7.2116ESE1013.00.0961812.012.012.411.8False002.013.90.0
2025-04-16 01:00:0012.2False{'text': 'Mist', 'icon': '//cdn.weatherapi.com/weather/64x64/night/143.png', 'code': 1030}6.5118ESE1013.00.0962511.811.812.211.5False002.012.50.0
2025-04-16 02:00:0012.0False{'text': 'Mist', 'icon': '//cdn.weatherapi.com/weather/64x64/night/143.png', 'code': 1030}7.9123ESE1014.00.0951611.311.312.011.2False002.015.10.0
2025-04-16 03:00:0011.6False{'text': 'Mist', 'icon': '//cdn.weatherapi.com/weather/64x64/night/143.png', 'code': 1030}7.6127SE1014.00.0952811.011.011.610.8False002.014.70.0
2025-04-16 04:00:0011.4False{'text': 'Clear ', 'icon': '//cdn.weatherapi.com/weather/64x64/night/113.png', 'code': 1000}7.2124SE1014.00.0941810.810.811.410.5False0010.013.90.0
2025-04-16 05:00:0011.1False{'text': 'Clear ', 'icon': '//cdn.weatherapi.com/weather/64x64/night/113.png', 'code': 1000}7.2118ESE1014.00.0931310.510.511.110.0False0010.014.00.0
2025-04-16 06:00:0011.5True{'text': 'Sunny', 'icon': '//cdn.weatherapi.com/weather/64x64/day/113.png', 'code': 1000}7.2108ESE1015.00.0881310.910.911.59.7False0010.012.80.0
2025-04-16 07:00:0013.0True{'text': 'Sunny', 'icon': '//cdn.weatherapi.com/weather/64x64/day/113.png', 'code': 1000}7.9121ESE1015.00.0771212.612.613.09.2False0010.011.30.2
2025-04-16 08:00:0014.8True{'text': 'Sunny', 'icon': '//cdn.weatherapi.com/weather/64x64/day/113.png', 'code': 1000}10.1133SE1015.00.067914.314.314.88.8False0010.012.00.8
2025-04-16 09:00:0016.7True{'text': 'Sunny', 'icon': '//cdn.weatherapi.com/weather/64x64/day/113.png', 'code': 1000}11.2143SE1015.00.058716.716.716.78.4False0010.012.91.8
temp_cis_dayconditionwind_kphwind_degreewind_dirpressure_mbprecip_mmhumiditycloudfeelslike_cwindchill_cheatindex_cdewpoint_cwill_it_rainchance_of_rainchance_of_snowvis_kmgust_kphuv
datetime
2025-04-18 14:00:0012.9True{'text': 'Light drizzle', 'icon': '//cdn.weatherapi.com/weather/64x64/day/266.png', 'code': 1153}13.3167SSE1013.00.43898511.611.612.911.1True10002.017.10.3
2025-04-18 15:00:0013.0True{'text': 'Cloudy ', 'icon': '//cdn.weatherapi.com/weather/64x64/day/119.png', 'code': 1006}6.1166SSE1013.00.00867812.812.813.010.6False0010.08.00.3
2025-04-18 16:00:0013.6True{'text': 'Partly Cloudy ', 'icon': '//cdn.weatherapi.com/weather/64x64/day/116.png', 'code': 1003}4.7131SE1013.00.00823913.913.913.610.5False0010.05.90.2
2025-04-18 17:00:0014.5True{'text': 'Patchy rain nearby', 'icon': '//cdn.weatherapi.com/weather/64x64/day/176.png', 'code': 1063}7.6112ESE1013.00.01777614.314.314.510.5True76010.09.40.6
2025-04-18 18:00:0013.8True{'text': 'Patchy rain nearby', 'icon': '//cdn.weatherapi.com/weather/64x64/day/176.png', 'code': 1063}6.890E1013.00.04815013.713.713.810.7False70010.010.30.1
2025-04-18 19:00:0012.9True{'text': 'Partly Cloudy ', 'icon': '//cdn.weatherapi.com/weather/64x64/day/116.png', 'code': 1003}6.5102ESE1013.00.00852712.712.712.910.5False0010.011.30.0
2025-04-18 20:00:0012.5False{'text': 'Clear ', 'icon': '//cdn.weatherapi.com/weather/64x64/night/113.png', 'code': 1000}6.5113ESE1014.00.00872112.312.312.510.4False0010.011.10.0
2025-04-18 21:00:0012.1False{'text': 'Partly Cloudy ', 'icon': '//cdn.weatherapi.com/weather/64x64/night/116.png', 'code': 1003}6.191E1014.00.00893111.811.812.110.3False0010.010.30.0
2025-04-18 22:00:0011.8False{'text': 'Partly Cloudy ', 'icon': '//cdn.weatherapi.com/weather/64x64/night/116.png', 'code': 1003}5.0104ESE1014.00.00914211.711.711.810.3False0010.08.40.0
2025-04-18 23:00:0011.6False{'text': 'Partly Cloudy ', 'icon': '//cdn.weatherapi.com/weather/64x64/night/116.png', 'code': 1003}3.2122ESE1014.00.00914212.212.211.610.2False0010.05.50.0